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Personalization (Input (Customer Implicit Data (Behavior data (Customer…
Personalization
Input
Customer
Explicit Data
Mandatory
Name
Phone#
Email
Optional
Married ?
etc
Incremental
Customer
Implicit Data
Behavior
data
Customer
historical data
Loyalty programs
Customer
Click stream
(Web Analytics - Core metrics)
Ads Clicked
Search Engine
customer data
Referring
Website
Shopping
trends
Contextual
Holidays
Weather
Device type
Location
Geo fencing
Geo targeting
Seasonality
Competition
Distribution
Economy
Brand Information
Events
Goals
Incentives
/Promotions
Customer
Single Identity
Shipping/Delivery
Returns/Exchanges
1st Party Data
2nd Party Data
3rd party data
Declared
Observed
(Online activity,
Products bought, etc)
Inferred (By interests
/Preferences)
Modeled
Technology
Data
Persistence
Data
Lake
MAPR
CRM
Redshift
BigQuery
S3
MySQL
Data
Source
Real-time
Batch
External sources
Applications
POS
Web
Data
Pipeline
Spark/Kafka
Snowplow
Kinesis
Beam
Flink
ML/AI
R Server
SAS Server
Code
Development
Github
JIRA
Python/Java
Code
Deployment
Docker
Kubernetes
Visualization
Kibana/Grafana
Tableau
D3.js
Jupyter
Customer
Identity
Authentication
Authorization
Privileges
Anonymous
Capabilities
for Personalization
Marketing
Advertising
Rule-based
grouping
Email
Direct Mail
Brand Mkt
Types of
Customers to
target
Individual Content
Personalization
Anonymous
user
Customer
segmentation
based
Data Science
Models
Flexibility to
accommodate
in-house
models
Built-in
models
Customer
Segementation
Value based
clustering -
Purchases
Geo-based
Recommendations
Content
Product :
Personalized
Offers
KPIs (Measure success/failure of
Personalization)
Consistency
Across
Brands
Across
channels
Output
Web/Mobile/Store
Personalization
Predictive
Search/Discovery
Contextual
Product
recommendations
Real-time
targeting
Offers/Promotions
Channel specific
personalized data
Marketing
Email
Coupons
Promotions
Offline media
Online media
Visualization
/Reporting
Event
tracking
Goals
tracking
Integrated online
and store analytics
Chatbots
Machine Vision
Instore Visual
monitoring
Visual search
for detecting
shopping trends
from Social
media images
Size/Styling
(3D)
Process/
Journey map
Web JM
Testing
Multivariate
testing
Contextualized
testing
Customer
Journey
5 Personas
Mobile JM
Store JM
ML and AI
Algos/Models
Allocation
MCMC
Customer
Classification
K-Means
K-nearest neighbour
SVM
Naive Bayes
Digital media
Marketing
Market mix modelling
Bayesian
Vector Autoregression
Market Basket
Associative learning
Recommendation
Content
Filtering Algo
Collaborative
filtering algo
Data Sets
Training Data set
Reduced
number of dimensions
Raw data
Cleaned data
Time series
Products
Vendors
Certona
Dynamic Yield
Groupby
Everguage
Reflektion
Reqd. Product
Capabilities/
Questions to vendors
Real-time
model refresh ?
Batch (nightly)
model refresh?
Data
ingestion
Have APIs
for integration
Have a
native integration
to salesforce (CRM), etc ?
Intelligence
basis ?
location ?
Even Anonymous user?
Who visited site 6 times?
Data elements used?
How long are data elements
are stored for a customer ?
Configurable
customer
segmentation ?
Ability to stitch
data across channels ?
How is customer
identified?
Plugin to email
software ?
Product search
Intelligence ?
AI/ML Algos used?
Ability to track
Events, Goals, etc?